Variable definitions


The ticks_w_weather dataset contains the following variables:

  • site_id: The four-letter NEON site code
  • date: The first day of the MMWR week for this row
  • jd: The Julian date calculated from the date column
  • mmwr_year: The year of the MMWR week
  • mmwr_week: The week number of the MMWR week
  • mean_temp: The mean temperature of the current MMWR week. Calculated by taking the mean of daily mean temperatures
  • min_temp: The minimum temperature of the current MMWR week. Calculated by taking the minimum of daily minimum temperatures
  • max_temp: The maximum temperature of the current MMWR week. Calculated by taking the maximum of daily maximum temperatures
  • mean_rh: The mean relative humidity (%) of the current MMWR week. Calculated by taking the mean of daily mean RH values
  • rh_min: The minimum relative humidity (%) of the current MMWR week. Calculated by taking the minimum of daily minimum RH values
  • rh_max: The maximum relative humidity (%) of the current MMWR week. Calculated by taking the maximum of daily maximum RH values
  • mean_vpd: The mean vapor pressure deficit for the current MMWR week. Calculated by taking the mean of daily mean VPD values
  • mean_precip_mm: The mean daily precipitation (mm) for the current MMWR week. Calculated by taking the mean of daily precipitation sums
  • sum_precip_mm: Sum of daily precipitation (mm) for the current MMWR week. Calculated by summing the daily precipitation sums
  • dd: The average daily degree days accumulated over the current MMWR week
  • thirty_day_dd: The mean 30-day-rolling-sum of degree days for the current MMWR week. Calculated by taking the mean of daily thirty-day rolling sums
  • dd_30d_rollsum_lag34: The mean 30-day-rolling-sum of degree days for the MMWR week 34 weeks previous. To calculate: 1) Get thirty_day_dd calculations, 2) get the 34-week lagged values of those (i.e., lag(x = thirty_day_dd, n = 34L)), 3) average the values for all days in the current MMWR week
  • dd_30d_rollsum_lag42: Same as lag_thirty_day_dd_34wk, but 42 weeks previous
  • dd_30d_rollsum_lag50: Same as lag_thirty_day_dd_34wk, but 50 weeks previous
  • dd_30d_rollsum_prev_week: This is the thirty-day rolling sum of degree days from the last day of the previous MMWR week. No averaging takes place. e.g, if today is Sunday, then this is the 30-day rolling degree day sum of yesterday (Saturday)
  • cume_dd_prev_week: Similar to dd_30d_rollsum_prev_week, except this is the cumulative degree day count (starting Jan. 1) of the the last day of the previous MMWR week
  • cume_cd_prev_winter: The total number of “chill days” (see method below under Notes) from September 1st of the preceding year to March 31st of the current year. Values before April 1st use the previous year’s accumulations
  • amblyomma_americanum: The density of Amblyomma americanum ticks for the current MMWR week, reported as ticks per 1600m2
  • amam_filled: A version of the tick count column above that has been gap filled using linear interpolation
  • tick_interp_flag: A flag column that indicates whether the week’s value for amam_filled was observed or interpolated
  • amam_4wk_rollmean_lag1: The four-week rolling average of the interpolated tick count column, then lagged by one week
  • mean_vpd_4wk_rollmean_lag1: The four-week rolling average of the mean_vpd column, then lagged by one week
  • amam_4wk_rollmean_lag50: The four-week rolling average of the interpolated tick count column, then lagged by 50 weeks
  • mean_vpd_4wk_rollmean_lag50: The four-week rolling average of the mean_vpd column, then lagged by 50 weeks


Notes:

  • Degree days: mean(min_temp, max_temp) - 0. Then, if the results is positive, this is the number of degree days accumulated that day. Negative values (i.e., temps below 0C) do not count towards this.
  • Chill days = 0 - mean(min_temp, max_temp). Then, if the result is positive, this is the number of chill days accumulated that day. Negative values (i.e., temps above 0C) do not count towards this.


Dataset preview

site_id date jd mmwr_year mmwr_week mean_temp min_temp max_temp rh_min rh_max mean_vpd mean_precip_mm sum_precip_mm dd thirty_day_dd dd_30d_rollsum_lag34 dd_30d_rollsum_lag42 dd_30d_rollsum_lag50 dd_30d_rollsum_prev_week cume_dd_prev_week cume_cd_prev_winter amblyomma_americanum dd_rollsum_prev_week amam_filled tick_interp_flag amam_4wk_rollmean_lag1 mean_vpd_4wk_rollmean_lag1 amam_4wk_rollmean_lag50 mean_vpd_4wk_rollmean_lag50 amam_lag1 amam_lag2 amam_lag3 amam_lag4
BLAN 2015-04-19 109 2015 16 11.36429 1.25 26.55 25.6 100.0 0.7171429 4.6848571 32.794 11.36429 335.0286 101.1143 45.05714 28.60000 303.50 459.85 227.95 0.000000 107.65 0.000000 original 4.907976 0.8864286 4.907976 0.8864286 0.000000 0.000000 0.000000 0.000000
BLAN 2015-04-26 116 2015 17 13.03571 2.85 23.15 28.6 96.9 0.7571429 3.3024286 23.117 13.03571 369.4929 135.1643 94.20714 39.95000 339.70 539.40 227.95 NA 79.55 3.271984 interpolated 4.907976 0.8864286 4.907976 0.8864286 0.000000 0.000000 0.000000 0.000000
BLAN 2015-05-03 123 2015 18 20.17857 9.05 29.65 23.7 100.0 1.1057143 0.6911429 4.838 20.17857 414.6643 185.3143 131.07857 87.17857 383.65 630.65 227.95 NA 91.25 6.543967 interpolated 4.907976 0.8864286 4.907976 0.8864286 3.271984 0.000000 0.000000 0.000000
BLAN 2015-05-10 130 2015 19 19.76429 5.55 30.65 37.0 100.0 0.9657143 0.3840000 2.688 19.76429 470.0214 245.2286 175.57857 126.60714 442.75 771.90 227.95 9.815951 141.25 9.815951 original 4.907976 0.8864286 4.907976 0.8864286 6.543967 3.271984 0.000000 0.000000
BLAN 2015-05-17 137 2015 20 17.82143 5.05 31.35 29.7 100.0 0.8371429 0.0000000 0.000 17.82143 502.1643 285.6500 239.02857 167.07143 486.05 910.25 227.95 NA 138.35 9.877301 interpolated 4.907976 0.8864286 4.907976 0.8864286 9.815951 6.543967 3.271984 0.000000
BLAN 2015-05-24 144 2015 21 23.27857 14.15 30.75 31.7 96.3 1.2085714 0.6142857 4.300 23.27857 563.6286 339.3714 277.38571 232.33571 511.40 1035.00 227.95 NA 124.75 9.938650 interpolated 7.377301 0.9164286 4.907976 0.8864286 9.877301 9.815951 6.543967 3.271984
BLAN 2015-05-31 151 2015 22 18.64286 12.05 31.45 44.6 100.0 0.5071429 7.1425714 49.998 18.64286 605.2000 375.1643 335.02857 269.83571 595.70 1197.95 227.95 10.000000 162.95 10.000000 original 9.043967 1.0292857 4.907976 0.8864286 9.938650 9.877301 9.815951 6.543967
BLAN 2015-06-07 158 2015 23 24.45000 13.85 33.35 36.2 100.0 1.2314286 1.3054286 9.138 24.45000 609.3500 423.9071 369.49286 329.85714 597.00 1328.45 227.95 19.393939 130.50 19.393939 original 9.907975 0.8796429 4.907976 0.8864286 10.000000 9.938650 9.877301 9.815951
BLAN 2015-06-14 165 2015 24 25.69286 19.55 32.15 45.7 92.2 1.1871429 4.3777143 30.644 25.69286 650.7429 475.8786 414.66429 363.21429 632.95 1499.60 227.95 NA 171.15 11.265597 interpolated 12.302473 0.9460714 4.907976 0.8864286 19.393939 10.000000 9.938650 9.877301
BLAN 2015-06-21 172 2015 25 23.82143 15.95 34.35 40.8 100.0 1.0314286 0.3840000 2.688 23.82143 698.1357 506.6500 470.02143 406.22143 673.95 1679.45 227.95 3.137255 179.85 3.137255 original 12.649547 1.0335714 4.907976 0.8864286 11.265597 19.393939 10.000000 9.938650



Quality control plots


Missing data:


Variables vs. data source:


Minimum temp

Timeseries

1:1


Maximum temp

Timeseries

1:1


Minimum RH

Timeseries

1:1


Maximum RH

Timeseries

1:1


VPD

Timeseries

1:1


Precip

Timeseries

1:1

Tick counts


Tick count line plot:


Raw tick counts overlaid on interpolation:


Chill days


Workflow diagram

tar_visnetwork()
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